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Arxiv.org
Sep 22, 2013 Eric F. Lock; David B. Dunson
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The task of clustering a set of objects based on multiple sources of data arises in several modern applications. We propose an integrative statistical model that permits a separate clustering of the objects for each data source. These separate clusterings adhere loosely to an overall consensus clustering, and hence they are not independent. We describe a computationally scalable Bayesian framework for simultaneous estimation of both the consensus clustering and the source-specific clusterings....
Source: http://arxiv.org/abs/1302.7280v1